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Article

Genetic Diversity and Genome-Wide Association Study for Shoot and Root Traits in Rice Grown Under Water Deficit at Early Vegetative Stage

by
Gabriel Brandão das Chagas
,
Rodrigo Pagel Machado
,
Célanet Fils-Aimé
,
Antônio de Azevedo Perleberg
,
Viviane Kopp da Luz
,
Antonio Costa de Oliveira
,
Luciano Carlos da Maia
* and
Camila Pegoraro
*
Department of Plant Breeding, Federal University of Pelotas, Capão do Leão 96160-000, Rio Grande do Sul, Brazil
*
Authors to whom correspondence should be addressed.
Stresses 2025, 5(1), 5; https://doi.org/10.3390/stresses5010005
Submission received: 4 December 2024 / Revised: 30 December 2024 / Accepted: 7 January 2025 / Published: 13 January 2025
(This article belongs to the Collection Feature Papers in Plant and Photoautotrophic Stresses)
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Abstract

:
Water deficit affects rice growth, development, and yield. Knowledge of genetic diversity for water deficit tolerance, as well as the genetic architecture that is responsible for this trait, can accelerate rice cultivars’ improvement. In this study, different tools were applied to assess genetic diversity and identify genome regions associated with shoot and root traits in rice germplasm grown under water deficit at an early vegetative stage. A panel of 177 rice genotypes grown under water deficit was evaluated for root length (RL), root dry weight (RDW), shoot length (SL), and shoot dry weight (SDW). Genetic diversity was investigated using means grouping and principal component analysis. For the genome-wide association study, a general linear model was applied, using RL, RDW, SL, and SDW phenotypic data converted into Best Linear Unbiased Prediction (BLUPs); genotypic data (1185 single nucleotide polymorphism—SNPs-loci); and population structure. Overall, little genetic diversity was observed, but genotypes with a higher water deficit tolerance were identified. Several significant SNPs were mapped, 81, 5, 53, and 41 for RL, RDW, SL, and SDW, respectively. Among the identified genes, there are those encoding kinases, proteins involved in phytohormone and cell wall metabolism, and Cytochrome P450. The obtained results provide insight into genetic diversity and the genetic architecture of water deficit tolerance, which will be useful in improving this trait in rice grown in Brazil.

1. Introduction

More than half of the world’s population consumes rice (Oryza sativa L.) as an essential food [1]. Brazil is among the top ten rice-producing countries [2], and Rio Grande do Sul state is responsible for ~70% of the national production [3]. Rice is vulnerable to different abiotic stresses, and water deficit is one example of them [4]. Rain-fed ecosystems are more exposed to water scarcity [1]. However, because an irrigated ecosystem depends on water availability, it can also be affected by drought episodes. In Rio Grande do Sul, rice is cultivated using permanent flooding, and in recent years, during La Niña events (crop season 2021/2022 and 2022/2023), the crop has experienced a lack of water due to prolonged droughts [5].
The exposure period and the moment in which water deficit occurs determine the impact on rice yield. The most sensitive stages are mid-tillering, flowering, and panicle initiation [4]. Furthermore, rice is also sensitive at early stages, with severe reductions in germination and seedling growth [6]. Leaf and root development are significantly affected by water deficit at the seedling stage [7]. One of the mechanisms for dealing with water deficit is the plant’s ability to modify its roots to have greater thickness and length. There is evidence that this happens due to the relocation of assimilates of the root instead of the shoot. Nevertheless, root growth can also be reduced under drought. Therefore, root response under water deficit depends on genotype, exposure time, and stress intensity [4].
The creation of rice cultivars tolerant to water deficit is among the strategies to reduce losses caused by this problem. Breeding progress for this trait in rice is slow due to narrow genetic diversity [6]. Therefore, screening studies for water deficit tolerance on local rice germplasm are fundamental. Furthermore, the identification of genes associated with water deficit tolerance can also help breeders, since it is a complex trait, presents low heritability, and is determined by many genes [8].
Genome-wide association study (GWAS) is a tool used to identify candidate genes that control tolerance to water deficit in rice [9,10,11,12,13,14,15,16,17,18,19]. However, information on rice germplasm in Brazil is limited. Considering that water deficit tolerance is controlled by many genes and other environmental factors, research on germplasm and local conditions are essential for rice breeders in Brazil.
The present study evaluated genetic diversity and identified genome regions associated with shoot and root traits in rice germplasm used in Brazil that was grown under water deficit at an early vegetative stage.

2. Results

2.1. Genetic Diversity in Rice for Water Deficit Tolerance at Early Vegetative Stage

Different root lengths (RL) were observed in rice plants grown under water deficit, and genotypes were separated into seven groups. Group ‘a’ is formed by just one genotype (102), which presented the highest RL (33.95 cm). Two genotypes (130 and 149) were allocated to group ‘b’, with an RL of approximately 28 cm. Group ‘c’ has three genotypes (23, 110, 151) with roots measuring from 23 cm to 24 cm. The largest group is ‘d’, with 92 genotypes that have RLs from 16.99 cm to 21.93 cm. The second largest group is ‘e’, made up of 54 genotypes that have roots measuring from 13.69 cm to 16.53 cm. Group ‘f’ contains 21 genotypes, whose RLs vary from 11.53 cm to 13.47 cm. Finally, group ‘g’ is composed of 4 genotypes, with the smallest RL ranging from 6.76 cm to 10.27 cm (Table S1).
The genotypes were divided into six groups in relation to root dry weight (RDW). In group ‘a’, there are only two genotypes (155 and 59), with the highest RDW values (0.0485 g to 0.0496 g). Group ‘b’ has 21 genotypes, with RDW values ranging from 0.0338 g to 0.0417 g. Groups ‘c’, ‘d’, and ‘e’ contain 47, 53, and 43 genotypes, respectively. Group ‘f’, composed of 11 genotypes, has the lowest RDW values (0.0061 g to 0.0133 g) (Table S2).
For shoot length (SL), only three groups were formed. Group ‘a’ is made up of 21 genotypes that present the highest SL, with values from 27.60 cm to 34.18 cm. In group ‘b’, there are 56 genotypes, whose SL varies from 23.05 cm to 27.26 cm. Group ‘c’ contains 100 genotypes, with SL from 12.79 cm to 22.94 cm (Table S3).
If shoot dry weight (SDW) is considered, four groups of genotypes were created. The highest SDW values (0.1358 g to 0.1833 g) are observed in group ‘a’, with 20 genotypes. The largest groups are ‘b’ and ‘c’, with 71 and 53 genotypes each, with values ranging from 0.0974 g to 0.1330 g in group ‘b’ and 0.0681 g to 0.0966 g in group ‘c’. Finally, group ‘d’ is formed by 33 genotypes with the lowest SDW (0.0272 g to 0.0664 g) (Table S4).
PCA required two components to explain 72.8% variation (CP1 48.1, CP2 24.7), and two large clusters were formed. Within C1, the most dispersed genotypes are 14, 54, 6, and 10, and the other ones form subclusters. In C2, genotypes 102, 130, 23, and 149 are highlighted, with the other ones allocated into subclusters (Figure 1).

2.2. Genome Regions Associated with Shoot and Root Traits in Rice Under Water Deficit at Early Vegetative Stage

For RL, 81 SNPs were mapped, and they are distributed across all 12 chromosomes (Figure 2a). Close to these SNPs there are 272 genes, and among those, there are genes encoding kinases such as Os01g0152600, Os01g0152687, Os01g0152800, Os01g0738300, Os03g0285800, and Os02g0126400. The last two genes have already been associated with water deficit response. Two other genes, whose enzymes are involved in the response to drought, were also detected (Os01g0738400: CCCH-zinc finger protein; Os04g0580400: SUMO conjugating enzyme). Furthermore, genes encoding enzymes involved in ethylene synthesis (Os05g0149300: 1-aminocyclopropane-1-carboxylate oxidase—ACO) and in cell cycle (Os09g0114500: kinesin-4, cell cycle and wall modification, cell elongation by regulating gibberellic acid—GA biosynthesis pathway) were found. Afterwards, Os01g0234300 (Pectinesterase) and Os05g0149000 (Pectinesterase inhibitor domain) genes, associated with cell wall metabolism, were observed (Table S6).
Only five SNPs related to RWD have been identified, which are located on chromosomes 1, 3, 5, 6, and 10 (Figure 2b). Adjacent to the SNPs, 14 genes were detected. Among the mapped genes, there are Os01g0234300 and Os01g0234499 and Pectinesterase encoders (Table S7).
For SL, 53 SNPs were observed, and they are distributed on almost all chromosomes, except on 10 (Figure 3a). In total, 145 genes linked to these SNPs were found. Among these, there are those kinases encoding or related to these proteins that receive, interact, and associate with them (Os01g0136900, Os03g0319400, Os03g0604401, Os09g0561500, Os09g0561600, Os12g0640700, and Os12g0640800). Genes correlated to auxin homeostasis (Os01g0178000) and auxin-independent growth promoter (Os12g0190100) were also identified. In addition to these, Cytochrome P450 family protein genes (Os12g0118900 and Os12g0119000) were detected. Furthermore, two genes related to water deficit, Os02g0527100 (Chromatin remodeling factor 710) and Os02g0115700 (Catalase A), were presented (Table S8).
When SDW is considered, 41 SNPs were found, and they are located on most chromosomes, except on 10 (Figure 3b). Next to SNPs, there are 116 genes, such as those encoding kinases or related to these proteins, which receive, interact and associate with them (Os01g0136800, Os01g0136900, Os01g0190400, Os03g0319100, and Os03g0319400). An auxin-independent growth promoter gene (Os12g0190100) was also identified. Besides these, Os02g0115700—Catalase A (drought tolerance), was present (Table S9).

3. Discussion

3.1. Genetic Diversity in Rice for Water Deficit Tolerance at Early Vegetative Stage

Distinct alleles of the same gene in different individuals within a species are responsible for genetic diversity. Genetic diversity studies provide information about the genetic structure of a population and act as a platform for selecting parents for breeding blocks [20]. Shoots and roots are responsible for defense mechanisms in response to abiotic stresses, and roots are the first organ in contact with water deficit [4]. In this study, some rice genotypes responded differently in terms of shoot and root traits under water restriction (Tables S1–S4, and Figure 1), indicating different levels of tolerance and presence of genetic diversity, even if narrow. This diversity can be explored by rice breeders. In research carried out with a germplasm collection from North India, with 114 rice genotypes, variation among genotypes for drought tolerance and for shoot and root traits was also verified [20]. Although studies point to narrowing diversity and genetic erosion in rice [21], there are still target genotypes for breeders.
Water deficit can affect root function by modifying growth and architecture, and altering the permeability of the cell wall (hydraulic). Under this stress, there is a concentration increase in abscisic acid (ABA) in roots, which promotes their elongation. However, reduced root length under water restriction has also been reported. Yet, in general, the rice genotypes that are most tolerant to water deficit are those that have longer and more abundant roots [22]. Thus, the highest RL of genotypes allocated to groups ‘a’, ‘b’, and ‘c’ (Table S1) tend to have a beneficial effect in dealing with water deficit, as they make it possible to reach water in the deeper soil layers. Under drought, root dry mass is reduced [22]. In that regard, genotypes with higher RDW from groups ‘a’ and ‘b’ (Table S2) may also have a higher tolerance. In this germplasm, genotypes 23, 102, 110, and 153, which present high RL and RDW values, could be promising for water deficit tolerance.
At early vegetative stages, a lack of water causes a reduction in plant growth [22]. This happens because stress promotes the interruption of cell division and elongation, in addition to impairing nutrient uptake, causing changes in the assimilate partitioning of plant organs [23]. Thus, genotypes with higher SL, allocated to group ‘a’ (Table S3), may present a higher tolerance. Rice shoot biomass is also reduced under water deficit stress [23]. Therefore, higher tolerance levels can be found in genotypes with lower SDW reduction, such as those present in group ‘a’ (Table S4). Five genotypes (39, 73, 83, 143, and 177) showed higher SL and SDW values under water restriction, which may be more tolerant. However, another tolerance mechanism to water deficit is the reduction in shoot/root growth ratio. It aims to reduce plant aerial parts where water loss occurs, and increases root growth, responsible for water capture [24]. In this case, genotypes with lower SL and higher RL would be the most tolerant, such as genotype 130, which has a long root and reduced shoot length.
Within each cluster created by PCA, most genotypes are similar, with some exceptions (Figure 1). Genotype 102 is distinct from the others due to the higher values of RL, RDW, and SL under water deficit. Likewise, genotype 149, which is distant, has superior values for RL and SL. The dispersion of genotype 23 occurs because of the greater values of RL and RDW. These profiles suggest that the genotypes above are promising for water deficit tolerance. Genotypes 6, 14, and 54, also dispersed from the others, show lower values for all assessed traits and demonstrate great sensitivity.

3.2. Genome Regions Associated with Shoot and Root Traits in Rice Under Water Deficit at Early Vegetative Stage

A relatively high number of SNP markers associated with RL under water deficit at early vegetative stages were detected (Figure 2a). In studies without water restriction, a large number of SNP markers linked to this trait were also observed [25,26]. However, in adult plants grown under water deficit, only four SNPs for RL were found [11]. Interestingly, in a previous study with the same collection as the one used in this research, only seven SNPs linked to this trait were identified. Yet, they had an evaluation at the end of the reproductive stage and without imposing water restrictions [27]. These results evidence the complexity of RL control, which is influenced by genotype, environment/water deficit, and the development stage. The previous study had already demonstrated the large number of QTLs involved in root system control under water restriction [28].
The identification of genes encoding kinases (Table S6) indicates the action of signaling cascades involved in drought tolerance, as previously shown [29]. These genes may be new candidates for controlling tolerance to this stress in rice.
The mapping of genes encoding 1-aminocyclopropane-1-carboxylate oxidase/ACO (ethylene biosynthesis) and kinesin-4 (cell cycle) (Table S6) reinforces ethylene importance in root growth and development, as well as tolerance to water deficit in rice [30]. Root elongation is determined by cell division in root apical meristem (RAM) and cell elongation. Ethylene inhibits cell proliferation in RAM to restrict root elongation in rice. Furthermore, it promotes gibberellins metabolism in roots, which further controls cell proliferation in RAM and primary root elongation [30,31]. However, under water deficit, the increase in ethylene synthesis promotes root growth in rice seedlings and improves tolerance to this stress [32]. Thus, genes whose products are involved in ethylene synthesis and cell division may be associated with water deficit tolerance in rice.
Few SNPs for RDW were found (Figure 2b). Similarly, previous studies [33,34] also identified a reduced number of SNPs for this trait. Furthermore, when adult plants from the same collection without water deficit were assessed, only four markers for RDW were mapped [27].
Regarding mapped genes, there are those encoding Pectinesterase or Pectin methylesterases (PMEs), which appear in both RL and RDW, and Pectinesterase inhibitors, identified for RL (Tables S6 and S7). Pectin is a cell wall constituent, which includes homogalacturonan (HG), rhamnogalacturonan I (RG-I), rhamnogalacturonan II (RG-II), xylogalacturonan (XGA), apiogalacturonan (AGA), arabinan, galactan, arabinogalactan I (AGI), and arabinogalactan II (AGII) [35]. This polymer can be converted into pectate and methanol by PME enzymes. Pectins are able to be remodeled when exposed to drought, increasing plasticity, which can contribute to the maintenance of cell turgor and symplast volume. The greater plasticity may be related to higher drought tolerance, due to an increase in RG-I and RG-II, possibly because pectin forms hydrated gels that limit cell damage [36,37,38]. Based on this, genes encoding PME or PME inhibitors identified may contribute to water deficit tolerance in rice.
Approximately 50 SNPs for each SL and SDW trait have been identified (Figure 3a,b). More than 500 SNPs for SL in rice seedlings without imposing water restrictions were previously mapped [39]. On the contrary, another study [34] found only 12 and 7 SNPs for SL and SDW, respectively, in seedlings grown without stress. This divergence shows the importance of germplasm, environment, and marker platform in the analysis.
Genes encoding protein kinases were again identified for rice shoots under water deficit (Tables S8 and S9). Different studies have already revealed the involvement of these proteins in drought tolerance [40,41,42], highlighting their importance for this trait.
Cytochrome P450 family proteins (CYPs), found for SL (Table S8), are associated with Nicotinamide adenine dinucleotide phosphate (NADPH)- and oxygen (O2)-dependent hydroxylation processes. CYPs act in biosynthesis with phytohormones, fatty acids, sterols, cell wall components, biopolymers, and antioxidants, in addition to different defense compounds and xenobiotic metabolism [43,44]. Previous studies demonstrate the involvement of DSS1/CYP96B4 in growth and drought tolerance to rice plants, which occurs, at least partly, due to the accumulation of ABA and other metabolites [45]. The participation of CYPs in water deficit tolerance has also been reported in other species [43,44]. CYPs act on different metabolisms that influence tolerance. Therefore, the two CYP-encoding genes identified in this study may play a fundamental role in mechanisms of water deficit tolerance in rice.
Another gene detected for SL is related to auxin homeostasis (Table S8). Auxins are associated with drought tolerance, due to signaling for increasing main root length, lateral root growth, leaf expansion inhibition, stomatal density limitation, and stomatal opening control [24]. This regulation makes the auxin-related gene a new candidate for controlling water deficit tolerance.

4. Materials and Methods

4.1. Phenotyping

A panel of 177 rice genotypes (Oryza sativa L.) (Table S5) grown under water deficit at early vegetative stages was evaluated. The experiment was conducted in a grow room, with artificial lighting (16 h photoperiod) and controlled temperature (25 ± 3 °C). Plants of each genotype were cultivated in plastic containers with a capacity of 700 mL (Figure S1). MECPLANT substrate was used, with a composition of pine bark, vermiculite, and macronutrients, with acidity corrector. This substrate is indicated for seedling production and can be used for water deficit studies [46].
Soil received water from sowing until emergence (approximately three days after sowing), and subsequently, plants remained without irrigation for 20 days, when evaluation was carried out. Analog tensiometers were installed (Hidrosense, model HID38, Jundiaí, São Paulo, Brazil) at a depth of 0.10 m to monitor soil moisture. The soil tension reached −50 KPa on the 10th day without irrigation, reaching −500 KPa on the 20th day. The stress period was based on a pilot experiment, in which it was observed that 20 days was the maximum period that rice plants survived without irrigation. A randomized block design was used, with three replications, which consisted of a container having five plants.
Shoot (SL) and root (RL) length were evaluated using a graduated ruler. Subsequently, the shoot was separated from the roots and both were placed in independent paper bags and kept in a forced air oven at 70 °C until reaching constant weight. Next, root dry weight (RDW) and shoot dry weight (SDW) were measured, using a precision scale (Shimadzu, model auw220d, Kyoto, Japan) [47].

4.2. Statistical Analysis

The obtained data were tested for distribution, and once the normality of the residuals was verified, analysis of variance (ANOVA) was performed. Afterwards, means were grouped using the Scott-Knott test (p < 0.05). All the analyses were performed using R 4.3.3 software [48].
Additionally, a principal component analysis (PCA) was also performed, through which the number of components was determined by the explained variation proportion. The cluster number was defined by K-means and Silhouette score. Orange 3.38.1 software was applied to conduct these analyses [49].

4.3. Genotyping

The analyzed rice panel had already been applied in a previous study. Therefore, the information about genotyping, marker filtering, linkage disequilibrium (LD), genomic relationship matrix (kinship), and population structure is the same as that used by Chagas et al. [27].
Genotyping was performed at the International Rice Research Institute (IRRI), using the 7 K Infinium single nucleotide polymorphism (SNP) platform (Illumina®) with 7098 SNPs markers (https://isl.irri.org/services/genotyping/7k, accessed on 2 January 2025). After the first filtering in TASSEL 5.2.41 software [50], 4843 markers remained.
Linkage disequilibrium (LD) between different pairs of loci was calculated in pLINK 1.9 software [51] and the highest mean of r2 of 0.5645 was considered. A new filtering applying LD value was conducted in pLINK software, and only 1185 markers were left, which were used to build genetic relatedness (genomic relationship matrix or kinship) and population structure and to perform the genome-wide association study.
The genomic relationship matrix (K) was built based on Queller–Goodnight estimator [52] in Coancestry 1.0.1.2 software [53]. Population structure (Q) was simulated using Structure 2.3.4 software [54]. Results were interpreted in the Structure Harvester 0.6.93 software [55] and Evanno’s criterion was applied to define the optimal number of groups (ΔK) [56]. The studied panel was divided into two subpopulations.

4.4. Genome-Wide Association Study (GWAS)

The phenotyping data were transformed into Best Linear Unbiased Prediction (BLUPs), taking into account the Queller–Goodnight relationship matrix (K), as demonstrated by Chagas et al. [27]. To obtain BLUPs, the proc glimmix procedure was applied in SAS OnDemand for Academics software [57].
TASSEL V.5.2.41 software [50] was used to perform associative mapping analysis, applying the general linear model (GLM). This model includes phenotypic data (RL, RDW, SL, and SDW), genotypic data (1185 SNPs loci), and population structure (Q). To consider a SNP locus associated with phenotypic differences in root and shoot traits, values based on Bonferroni correction were established as cutoff points (−log10 p-Value = 0.05/nº SNPs) [58]. In this regard, the cutoff point was 4.3747. The results were displayed as Manhattan plots.
The dissection of chromosomal regions that present SNPs associated with shoot length, shoot dry weight, root length, and root dry weight was conducted based on the Rice Annotation Project Database/RAP-DB (http://rapdb.dna.affrc.go.jp/) (accessed on 1 May 2024). The physical location of each SNP on every chromosome was determined to enable the identification of candidate genes linked to significant SNPs. A 10 kb region on each side of the SNPs was analyzed [59].

5. Conclusions

The collection of rice genotypes used in Brazil presents genetic diversity for water deficit tolerance, even if it is small, which can be explored by breeders. Among the candidate genes associated with water deficit tolerance are those encoding kinases, proteins involved in phytohormone and cell wall metabolism, and Cytochrome P450. The newly identified genes provide insight into the genetic architecture of water deficit tolerance in rice at the vegetative stage.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/stresses5010005/s1, Table S1. Means grouping of root length (RL) in 177 rice genotypes (Gen) grown under water deficit at early vegetative stage; Table S2. Means grouping of root dry weight (RDW) in 177 rice genotypes (Gen) grown under water deficit at early vegetative stage; Table S3. Means grouping of shoot length (SL) in 177 rice genotypes (Gen) grown under water deficit at early vegetative stage; Table S4. Means grouping of shoot dry weight (SDW) in 177 rice genotypes (Gen) grown under water deficit at early vegetative stage; Table S5. Genotypes (Gen) used in this study. Table S6. Mapped genes to root length in rice using a diverse panel grown in Southern Brazil. Selected genes from Rice Annotation Project Database (RAP-DB) in 20 kb for each identified SNP; Table S7. Mapped genes to root dry weight in rice using a diverse panel grown in Southern Brazil. Selected genes from Rice Annotation Project Database (RAP-DB) in 20 kb for each identified SNP; Table S8. Mapped genes to shoot length in rice using a diverse panel grown in Southern Brazil. Selected genes from Rice Annotation Project Database (RAP-DB) in 20 kb for each identified SNP; Table S9. Mapped genes to shoot dry weight in rice using a diverse panel grown in Southern Brazil. Selected genes from Rice Annotation Project Database (RAP-DB) in 20 kb for each identified SNP. Figure S1. Experiment overview. (a) sowing; (b) plants under water deficit; (c) measurement of root length (RL); and (d) measurement of dry weight of the aerial part (SDW).

Author Contributions

Conceptualization, C.P.; methodology, G.B.d.C., R.P.M., C.F.-A., A.d.A.P. and V.K.d.L.; software, L.C.d.M. and G.B.d.C.; validation, C.P., L.C.d.M. and A.C.d.O.; formal analysis, G.B.d.C., R.P.M., C.F.-A., A.d.A.P., V.K.d.L. and L.C.d.M.; investigation, G.B.d.C., R.P.M., C.F.-A., A.d.A.P., V.K.d.L. and L.C.d.M.; resources, C.P., L.C.d.M. and A.C.d.O.; data curation, C.P., L.C.d.M. and A.C.d.O.; writing—original draft preparation, C.P.; writing—review and editing, C.P.; visualization, A.C.d.O.; supervision, C.P.; project administration, C.P.; funding acquisition, C.P., L.C.d.M., A.C.d.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) grant no. 401902/2016-1, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) grant no. 001 and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS).

Data Availability Statement

All data are fully available and included within the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Principal Component Analysis—PCA. Shoot length (SL), root length (RL), shoot dry weight (SDW), and root dry weight (RDW) traits in 177 rice genotypes subjected to water deficit at early vegetative stage. C1: cluster 1 (blue); C2: cluster 2 (red). The names of the genotypes corresponding to the numbers are in the Table S5.
Figure 1. Principal Component Analysis—PCA. Shoot length (SL), root length (RL), shoot dry weight (SDW), and root dry weight (RDW) traits in 177 rice genotypes subjected to water deficit at early vegetative stage. C1: cluster 1 (blue); C2: cluster 2 (red). The names of the genotypes corresponding to the numbers are in the Table S5.
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Figure 2. Manhattan plots of −Log10 (p) vs. chromosomal position of SNP markers. (a) SNP markers associated with root length (RL); and (b) SNP markers associated root dry weight (RDW).
Figure 2. Manhattan plots of −Log10 (p) vs. chromosomal position of SNP markers. (a) SNP markers associated with root length (RL); and (b) SNP markers associated root dry weight (RDW).
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Figure 3. Manhattan plots of −Log10 (p) vs. chromosomal position of SNP markers. (a) SNP markers associated with shoot length (SL); and (b) SNP markers associated with shoot dry weight (SDW).
Figure 3. Manhattan plots of −Log10 (p) vs. chromosomal position of SNP markers. (a) SNP markers associated with shoot length (SL); and (b) SNP markers associated with shoot dry weight (SDW).
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das Chagas, G.B.; Machado, R.P.; Fils-Aimé, C.; Perleberg, A.d.A.; da Luz, V.K.; Costa de Oliveira, A.; da Maia, L.C.; Pegoraro, C. Genetic Diversity and Genome-Wide Association Study for Shoot and Root Traits in Rice Grown Under Water Deficit at Early Vegetative Stage. Stresses 2025, 5, 5. https://doi.org/10.3390/stresses5010005

AMA Style

das Chagas GB, Machado RP, Fils-Aimé C, Perleberg AdA, da Luz VK, Costa de Oliveira A, da Maia LC, Pegoraro C. Genetic Diversity and Genome-Wide Association Study for Shoot and Root Traits in Rice Grown Under Water Deficit at Early Vegetative Stage. Stresses. 2025; 5(1):5. https://doi.org/10.3390/stresses5010005

Chicago/Turabian Style

das Chagas, Gabriel Brandão, Rodrigo Pagel Machado, Célanet Fils-Aimé, Antônio de Azevedo Perleberg, Viviane Kopp da Luz, Antonio Costa de Oliveira, Luciano Carlos da Maia, and Camila Pegoraro. 2025. "Genetic Diversity and Genome-Wide Association Study for Shoot and Root Traits in Rice Grown Under Water Deficit at Early Vegetative Stage" Stresses 5, no. 1: 5. https://doi.org/10.3390/stresses5010005

APA Style

das Chagas, G. B., Machado, R. P., Fils-Aimé, C., Perleberg, A. d. A., da Luz, V. K., Costa de Oliveira, A., da Maia, L. C., & Pegoraro, C. (2025). Genetic Diversity and Genome-Wide Association Study for Shoot and Root Traits in Rice Grown Under Water Deficit at Early Vegetative Stage. Stresses, 5(1), 5. https://doi.org/10.3390/stresses5010005

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